QE4DRaSLAM: Data-Augmented 4-D mmWave SLAM for Extremely Sparse Point Clouds

Autonomous navigation of robots has entered an era of robustness. Therefore, the demand for stable navigation in extreme environments is increasing. 4D millimeter-wave radar has a strong ability to resist environmental interference and can maintain accurate perception even in extreme weather conditions. It is an excellent solution. However, current research on navigation mainly focuses on vision-based and LiDAR-based methods, and the attention paid to 4D millimeter-wave radar (x, y, z, Doppler) radar systems is very limited. The current 4D millimeter wave SLAM system treats 4D millimeter wave data as LiDAR data and directly processes point cloud data. At the same time, the 4D millimeter wave radar point cloud has large noise, strong sparsity, and difficult to extract geometric features such as edges and planes, which poses a challenge to the establishment of a high-precision SLAM system. In addition, the data volume and cost of 4D millimeter wave radar are directly related. The cost of tens of thousands of dollars for high-end 4D millimeter wave radar directly restricts its application. Therefore, the construction of a SLAM system using low-cost and low-data-volume 4D millimeter wave radar has become an urgent problem to be solved. To address these problems, this paper proposes a data-enhanced low-cost 4D millimeter-wave radar SLAM system. The system introduces: (1) a point cloud SLAM quality assessment framework to establish the relationship between data quality and SLAM performance; (2) a data enhancement technique based on quality assessment to generate high-quality data from sparse, low-quality 4D radar input to meet positioning and mapping requirements; (3) a SLAM system enhanced by quality characteristics, incorporating velocity calibration into scan matching to improve accuracy, and fusing IMU pre-integration with Doppler velocity pre-integration to obtain a robust integration factor. In addition, a velocity segment-based loop closure mechanism is constructed in the SLAM system. Verification experiments conducted in real environments with dataset comparisons covering distances from 180 meters to 4.8 kilometers have demonstrated the scientificity and effectiveness of our approach, making a significant contribution to the promotion of low-cost 4D millimeter-wave radar applications.